{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Benchmarking Performance and Scaling of Python Clustering Algorithms\n",
    "\n",
    "There are a host of different clustering algorithms and implementations thereof for Python. The performance and scaling can depend as much on the implementation as the underlying algorithm. Obviously a well written implementation in C or C++ will beat a naive implementation on pure Python, but there is more to it than just that. The internals and data structures used can have a large impact on performance, and can even significanty change asymptotic performance. All of this means that, given some amount of data that you want to cluster your options as to algorithm and implementation maybe significantly constrained. I'm both lazy, and prefer empirical results for this sort of thing, so rather than analyzing the implementations and deriving asymptotic performance numbers for various implementations I'm just going to run everything and see what happens.\n",
    "\n",
    "To begin with we need to get together all the clustering implementations, along with some plotting libraries so we can see what is going on once we've got data. Obviously this is not an exhaustive collection of clustering implementations, so if I've left off your favourite I apologise, but one has to draw a line somewhere.\n",
    "\n",
    "The implementations being test are:\n",
    "\n",
    "* [Sklearn](http://scikit-learn.org/stable/modules/clustering.html) (which implements several algorithms):\n",
    " * K-Means clustering\n",
    " * DBSCAN clustering\n",
    " * Agglomerative clustering\n",
    " * Spectral clustering\n",
    " * Affinity Propagation\n",
    "* [Scipy](http://docs.scipy.org/doc/scipy/reference/cluster.html) (which provides basic algorithms):\n",
    " * K-Means clustering\n",
    " * Agglomerative clustering\n",
    "* [Fastcluster](http://danifold.net/fastcluster.html) (which provides very fast agglomerative clustering in C++)\n",
    "* [DeBaCl](https://github.com/CoAxLab/DeBaCl) (Density Based Clustering; similar to a mix of DBSCAN and Agglomerative)\n",
    "* [HDBSCAN](https://github.com/scikit-learn-contrib/hdbscan) (A robust hierarchical version of DBSCAN)\n",
    "\n",
    "Obviously a major factor in performance will be the algorithm itself. Some algorithms are simply slower -- often, but not always, because they are doing more work to provide a better clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
     ]
    }
   ],
   "source": [
    "import hdbscan\n",
    "import debacl\n",
    "import fastcluster\n",
    "import sklearn.cluster\n",
    "import scipy.cluster\n",
    "import sklearn.datasets\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set_context('poster')\n",
    "sns.set_palette('Paired', 10)\n",
    "sns.set_color_codes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need some benchmarking code at various dataset sizes. Because some clustering algorithms have performance that can vary quite a lot depending on the exact nature of the dataset we'll also need to run several times on randomly generated datasets of each size so as to get a better idea of the average case performance.\n",
    "\n",
    "We also need to generalise over algorithms which don't necessarily all have the same API. We can resolve that by taking a clustering function, argument tuple and keywords dictionary to let us do semi-arbitrary calls (fortunately all the algorithms do at least take the dataset to cluster as the first parameter).\n",
    "\n",
    "Finally some algorithms scale poorly, and I don't want to spend forever doing clustering of random datasets so we'll cap the maximum time an algorithm can use; once it has taken longer than max time we'll just abort there and leave the remaining entries in our datasize by samples matrix unfilled.\n",
    "\n",
    "In the end this all amounts to a fairly straightforward set of nested loops (over datasizes and number of samples) with calls to sklearn to generate mock data and the clustering function inside a timer. Add in some early abort and we're done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def benchmark_algorithm(dataset_sizes, cluster_function, function_args, function_kwds,\n",
    "                        dataset_dimension=10, dataset_n_clusters=10, max_time=45, sample_size=2):\n",
    "    \n",
    "    # Initialize the result with NaNs so that any unfilled entries \n",
    "    # will be considered NULL when we convert to a pandas dataframe at the end\n",
    "    result = np.nan * np.ones((len(dataset_sizes), sample_size))\n",
    "    for index, size in enumerate(dataset_sizes):\n",
    "        for s in range(sample_size):\n",
    "            # Use sklearns make_blobs to generate a random dataset with specified size\n",
    "            # dimension and number of clusters\n",
    "            data, labels = sklearn.datasets.make_blobs(n_samples=size, \n",
    "                                                       n_features=dataset_dimension, \n",
    "                                                       centers=dataset_n_clusters)\n",
    "            \n",
    "            # Start the clustering with a timer\n",
    "            start_time = time.time()\n",
    "            cluster_function(data, *function_args, **function_kwds)\n",
    "            time_taken = time.time() - start_time\n",
    "            \n",
    "            # If we are taking more than max_time then abort -- we don't\n",
    "            # want to spend excessive time on slow algorithms\n",
    "            if time_taken > max_time:\n",
    "                result[index, s] = time_taken\n",
    "                return pd.DataFrame(np.vstack([dataset_sizes.repeat(sample_size), \n",
    "                                               result.flatten()]).T, columns=['x','y'])\n",
    "            else:\n",
    "                result[index, s] = time_taken\n",
    "        \n",
    "    # Return the result as a dataframe for easier handling with seaborn afterwards\n",
    "    return pd.DataFrame(np.vstack([dataset_sizes.repeat(sample_size), \n",
    "                                   result.flatten()]).T, columns=['x','y'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison of all ten implementations\n",
    "\n",
    "Now we need a range of dataset sizes to test out our algorithm. Since the scaling performance is wildly different over the ten implementations we're going to look at it will be beneficial to have a number of very small dataset sizes, and increasing spacing as we get larger, spanning out to 32000 datapoints to cluster (to begin with). Numpy provides convenient ways to get this done via `arange` and vector multiplication. We'll start with step sizes of 500, then shift to steps of 1000 past 3000 datapoints, and finally steps of 2000 past 6000 datapoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset_sizes = np.hstack([np.arange(1, 6) * 500, np.arange(3,7) * 1000, np.arange(4,17) * 2000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it is just a matter of running all the clustering algorithms via our benchmark function to collect up all the requsite data. This could be prettier, rolled up into functions appropriately, but sometimes brute force is good enough. More importantly (for me) since this can take a significant amount of compute time, I wanted to be able to comment out algorithms that were slow or I was uninterested in easily. Which brings me to a warning for you the reader and potential user of the notebook: this next step is very expensive. We are running ten different clustering algorithms multiple times each on twenty two different dataset sizes -- and some of the clustering algorithms are slow (we are capping out at forty five seconds per run). That means that the next cell can take an hour or more to run. That doesn't mean \"Don't try this at home\" (I actually encourage you to try this out yourself and play with dataset parameters and clustering parameters) but it does mean you should be patient if you're going to!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "k_means = sklearn.cluster.KMeans(10)\n",
    "k_means_data = benchmark_algorithm(dataset_sizes, k_means.fit, (), {})\n",
    "\n",
    "dbscan = sklearn.cluster.DBSCAN(eps=1.25)\n",
    "dbscan_data = benchmark_algorithm(dataset_sizes, dbscan.fit, (), {})\n",
    "\n",
    "scipy_k_means_data = benchmark_algorithm(dataset_sizes, \n",
    "                                         scipy.cluster.vq.kmeans, (10,), {})\n",
    "\n",
    "scipy_single_data = benchmark_algorithm(dataset_sizes, \n",
    "                                        scipy.cluster.hierarchy.single, (), {})\n",
    "\n",
    "fastclust_data = benchmark_algorithm(dataset_sizes, \n",
    "                                     fastcluster.linkage_vector, (), {})\n",
    "\n",
    "hdbscan_ = hdbscan.HDBSCAN()\n",
    "hdbscan_data = benchmark_algorithm(dataset_sizes, hdbscan_.fit, (), {})\n",
    "\n",
    "debacl_data = benchmark_algorithm(dataset_sizes, \n",
    "                                  debacl.geom_tree.geomTree, (5, 5), {'verbose':False})\n",
    "\n",
    "agglomerative = sklearn.cluster.AgglomerativeClustering(10)\n",
    "agg_data = benchmark_algorithm(dataset_sizes, \n",
    "                               agglomerative.fit, (), {}, sample_size=4)\n",
    "\n",
    "spectral = sklearn.cluster.SpectralClustering(10)\n",
    "spectral_data = benchmark_algorithm(dataset_sizes, \n",
    "                                    spectral.fit, (), {}, sample_size=6)\n",
    "\n",
    "affinity_prop = sklearn.cluster.AffinityPropagation()\n",
    "ap_data = benchmark_algorithm(dataset_sizes, \n",
    "                              affinity_prop.fit, (), {}, sample_size=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to plot the results so we can see what is going on. The catch is that we have several datapoints for each dataset size and ultimately we would like to try and fit a curve through all of it to get the general scaling trend. Fortunately [seaborn](http://stanford.edu/~mwaskom/software/seaborn/) comes to the rescue here by providing `regplot` which plots a regression through a dataset, supports higher order regression (we should probably use order two as most algorithms are effectively quadratic) and handles multiple datapoints for each x-value cleanly (using the `x_estimator` keyword to put a point at the mean and draw an error bar to cover the range of data)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1125dee50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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Rt+akuhmOFCgmCCEmRBfour47q75/smvlMLrNLKTN/Gzk+cPuXdu3kKFEr6McoULX9Z7A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\nIlQAAAAtQgUAANAiVAAAAC1CBQAA0PIAlCbLIWNQ318AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10431be50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='x', y='y', data=k_means_data, order=2, \n",
    "            label='Sklearn K-Means', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=dbscan_data, order=2, \n",
    "            label='Sklearn DBSCAN', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=scipy_k_means_data, order=2, \n",
    "            label='Scipy K-Means', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=hdbscan_data, order=2, \n",
    "            label='HDBSCAN', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=fastclust_data, order=2, \n",
    "            label='Fastcluster Single Linkage', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=scipy_single_data, order=2, \n",
    "            label='Scipy Single Linkage', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=debacl_data, order=2, \n",
    "            label='DeBaCl Geom Tree', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=spectral_data, order=2, \n",
    "            label='Sklearn Spectral', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=agg_data, order=2, \n",
    "            label='Sklearn Agglomerative', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=ap_data, order=2, \n",
    "            label='Sklearn Affinity Propagation', x_estimator=np.mean)\n",
    "plt.gca().axis([0, 34000, 0, 120])\n",
    "plt.gca().set_xlabel('Number of data points')\n",
    "plt.gca().set_ylabel('Time taken to cluster (s)')\n",
    "plt.title('Performance Comparison of Clustering Implementations')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few features stand out. First of all there appear to be essentially two classes of implementation, with DeBaCl being an odd case that falls in the middle. The fast implementations tend to be implementations of single linkage agglomerative clustering, K-means, and DBSCAN. The slow cases are largely from sklearn and include agglomerative clustering (in this case using Ward instead of single linkage).\n",
    "\n",
    "For practical purposes this means that if you have much more than 10000 datapoints your clustering options are significantly constrained: sklearn spectral, agglomerative and affinity propagation are going to take far too long. DeBaCl may still be an option, but given that the hdbscan library provides \"robust single linkage clustering\" equivalent to what DeBaCl is doing (and with effectively the same runtime as hdbscan as it is a subset of that algorithm) it is probably not the best choice for large dataset sizes.\n",
    "\n",
    "So let's drop out those slow algorithms so we can scale out a little further and get a closer look at the various algorithms that managed 32000 points in under thirty seconds. There is almost undoubtedly more to learn as we get ever larger dataset sizes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison of fast implementations\n",
    "\n",
    "Let's compare the six fastest implementations now. We can scale out a little further as well; based on the curves above it looks like we should be able to comfortably get to 60000 data points without taking much more than a minute per run. We can also note that most of these implementations weren't that noisy so we can get away with a single run per dataset size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "large_dataset_sizes = np.arange(1,16) * 4000\n",
    "\n",
    "hdbscan_boruvka = hdbscan.HDBSCAN(algorithm='boruvka_kdtree')\n",
    "large_hdbscan_boruvka_data = benchmark_algorithm(large_dataset_sizes, \n",
    "                                         hdbscan_boruvka.fit, (), {}, \n",
    "                                                 max_time=90, sample_size=1)\n",
    "\n",
    "k_means = sklearn.cluster.KMeans(10)\n",
    "large_k_means_data = benchmark_algorithm(large_dataset_sizes, \n",
    "                                         k_means.fit, (), {}, \n",
    "                                         max_time=90, sample_size=1)\n",
    "\n",
    "dbscan = sklearn.cluster.DBSCAN(eps=1.25, min_samples=5)\n",
    "large_dbscan_data = benchmark_algorithm(large_dataset_sizes, \n",
    "                                        dbscan.fit, (), {}, \n",
    "                                        max_time=90, sample_size=1)\n",
    "\n",
    "large_fastclust_data = benchmark_algorithm(large_dataset_sizes, \n",
    "                                           fastcluster.linkage_vector, (), {}, \n",
    "                                           max_time=90, sample_size=1)\n",
    "\n",
    "large_scipy_k_means_data = benchmark_algorithm(large_dataset_sizes, \n",
    "                                               scipy.cluster.vq.kmeans, (10,), {}, \n",
    "                                               max_time=90, sample_size=1)\n",
    "\n",
    "large_scipy_single_data = benchmark_algorithm(large_dataset_sizes, \n",
    "                                              scipy.cluster.hierarchy.single, (), {}, \n",
    "                                              max_time=90, sample_size=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again we can use seaborn to do curve fitting and plotting, exactly as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x116038bd0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mvDV0ZJoUCoAOrVoz9Yfv2X34IC1uaZSYvmnXDsqVLkOd6jX4+/gxt3N+XLmc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BO3NjAnfmXCstKwnjYT3hmVdt5VlXbyWaxIz9F4dGfO3ZfWxasXPW8QjdgUK1hPagLMCrgKAl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111dcb5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='x', y='y', data=large_k_means_data, order=2, \n",
    "            label='Sklearn K-Means', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_dbscan_data, order=2, \n",
    "            label='Sklearn DBSCAN', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_scipy_k_means_data, order=2, \n",
    "            label='Scipy K-Means', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_hdbscan_boruvka_data, order=2, \n",
    "            label='HDBSCAN Boruvka', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_fastclust_data, order=2, \n",
    "            label='Fastcluster Single Linkage', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_scipy_single_data, order=2, \n",
    "            label='Scipy Single Linkage', x_estimator=np.mean)\n",
    "\n",
    "plt.gca().axis([0, 64000, 0, 150])\n",
    "plt.gca().set_xlabel('Number of data points')\n",
    "plt.gca().set_ylabel('Time taken to cluster (s)')\n",
    "plt.title('Performance Comparison of Fastest Clustering Implementations')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clearly something has gone woefully wrong with the curve fitting for the scipy single linkage implementation, but what exactly? If we look at the raw data we can see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>24000.0</td>\n",
       "      <td>12.127519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>28000.0</td>\n",
       "      <td>18.367958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>32000.0</td>\n",
       "      <td>34.444517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36000.0</td>\n",
       "      <td>33.508459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>40000.0</td>\n",
       "      <td>122.456995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>44000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>48000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>52000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>56000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>60000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x           y\n",
       "5   24000.0   12.127519\n",
       "6   28000.0   18.367958\n",
       "7   32000.0   34.444517\n",
       "8   36000.0   33.508459\n",
       "9   40000.0  122.456995\n",
       "10  44000.0         NaN\n",
       "11  48000.0         NaN\n",
       "12  52000.0         NaN\n",
       "13  56000.0         NaN\n",
       "14  60000.0         NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "large_scipy_single_data.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that at around 44000 points we hit a wall and the runtimes spiked. A hint is that I'm running this on a laptop with 8GB of RAM. Both single linkage algorithms use `scipy.spatial.pdist` to compute pairwise distances between points, which returns an array of shape `(n(n-1)/2, 1)` of doubles. A quick computation shows that that array of distances is quite large once we nave 44000 points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.211998105049133"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "size_of_array = 44000 * (44000 - 1) / 2         # from pdist documentation\n",
    "bytes_in_array = size_of_array * 8              # Since doubles use 8 bytes\n",
    "gigabytes_used = bytes_in_array / (1024.0 ** 3)  # divide out to get the number of GB\n",
    "gigabytes_used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we assume that my laptop is keeping much other than that distance array in RAM then clearly we are going to spend time paging out the distance array to disk and back and hence we will see the runtimes increase dramatically as we become disk IO bound. If we just leave off the last element we can get a better idea of the curve, but keep in mind that the scipy single linkage implementation does not scale past a limit set by your available RAM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/leland/.conda/envs/hdbscan_dev/lib/python2.7/site-packages/numpy/lib/polynomial.py:595: RankWarning: Polyfit may be poorly conditioned\n",
      "  warnings.warn(msg, RankWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x118843210>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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uZvv2rRw4sI/+/QcQGxvLL7+s4uGHH2PGjKmcOnWKqlWrMnDgi9Spc2uG7nvH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\nPwkhhBBCZGEx4Xdw12X+JoCZybJpA9qZ0zZlujd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5CwCv1qjF8k0bWLd9K41r10t3udbe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\n7/gU6m/egf74R2EnR+LGGOJ5z0d87bOx8sM7nmmt6cZxJqSNWd0VY0v9g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lxrjfAF0vcJ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\nCIIgCIIgCDNClApBEARBEARBEGbE/wf75BK/jkTJvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10433d5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='x', y='y', data=large_k_means_data, order=2, \n",
    "            label='Sklearn K-Means', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_dbscan_data, order=2, \n",
    "            label='Sklearn DBSCAN', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_scipy_k_means_data, order=2, \n",
    "            label='Scipy K-Means', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_hdbscan_boruvka_data, order=2, \n",
    "            label='HDBSCAN Boruvka', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_fastclust_data, order=2, \n",
    "            label='Fastcluster Single Linkage', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=large_scipy_single_data[:8], order=2, \n",
    "            label='Scipy Single Linkage', x_estimator=np.mean)\n",
    "\n",
    "plt.gca().axis([0, 64000, 0, 150])\n",
    "plt.gca().set_xlabel('Number of data points')\n",
    "plt.gca().set_ylabel('Time taken to cluster (s)')\n",
    "plt.title('Performance Comparison of Fastest Clustering Implementations')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we're looking for scaling we can write off the scipy single linkage implementation -- if even we didn't hit the RAM limit the $O(n^2)$ scaling is going to quickly catch up with us. Fastcluster has the same asymptotic scaling, but is heavily optimized to being the constant down much lower -- at this point it is still keeping close to the faster algorithms. It's asymtotics will still catch up with it eventually however.\n",
    "\n",
    "In practice this is going to mean that for larger datasets you are going to be very constrained in what algorithms you can apply: if you get enough datapoints only K-Means, DBSCAN, and HDBSCAN will be left. This is somewhat disappointing, paritcularly as [K-Means is not a particularly good clustering algorithm](http://nbviewer.jupyter.org/github/scikit-learn-contrib/hdbscan/blob/master/notebooks/Comparing%20Clustering%20Algorithms.ipynb), paricularly for exploratory data analysis.\n",
    "\n",
    "With this in mind it is worth looking at how these last several implementations perform at much larger sizes, to see, for example, when fastscluster starts to have its asymptotic complexity start to pull it away."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison of high performance implementations\n",
    "\n",
    "At this point we can scale out to 200000 datapoints easily enough, so let's push things at least that far so we can start to really see scaling effects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "huge_dataset_sizes = np.arange(1,11) * 20000\n",
    "\n",
    "k_means = sklearn.cluster.KMeans(10)\n",
    "huge_k_means_data = benchmark_algorithm(huge_dataset_sizes, \n",
    "                                        k_means.fit, (), {}, \n",
    "                                        max_time=120, sample_size=2, dataset_dimension=10)\n",
    "\n",
    "dbscan = sklearn.cluster.DBSCAN(eps=1.5)\n",
    "huge_dbscan_data = benchmark_algorithm(huge_dataset_sizes, \n",
    "                                       dbscan.fit, (), {},\n",
    "                                       max_time=120, sample_size=2, dataset_dimension=10)\n",
    "\n",
    "huge_scipy_k_means_data = benchmark_algorithm(huge_dataset_sizes, \n",
    "                                              scipy.cluster.vq.kmeans, (10,), {}, \n",
    "                                              max_time=120, sample_size=2, dataset_dimension=10)\n",
    "\n",
    "hdbscan_boruvka = hdbscan.HDBSCAN(algorithm='boruvka_kdtree')\n",
    "huge_hdbscan_data = benchmark_algorithm(huge_dataset_sizes, \n",
    "                                       hdbscan_boruvka.fit, (), {}, \n",
    "                                        max_time=240, sample_size=4, dataset_dimension=10)\n",
    "\n",
    "huge_fastcluster_data = benchmark_algorithm(huge_dataset_sizes, \n",
    "                                            fastcluster.linkage_vector, (), {}, \n",
    "                                            max_time=240, sample_size=2, dataset_dimension=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11d2aff50>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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usq0xY0byxBMVGT/+M+rWbcCsWdPYtWsHoAOysWM/5NlnmzFx4hQ6duzCd98t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\n+kXq0uy4YTmFcoW7p9JNjXTXhuuHu0hG3havdZ5Rw7n+2jTUM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A88J1x/HkZ0frpuv3dj\nfrNzXZMvRupbwAirwdbZGQ3LfY9S6nFKKUtrPa+1vkZr/RfnOhlBEISdjAgYQRCELUBr/RVMt59f\nVUo9YROKnK5bLzdJq4TL6L19Qmudqcv3YLi8BLgs/P4mjIFd/UwD/zUsaySy78w66noJMKW1TkcT\nQ6/Qg5iYmPPhEkxXsHp0zYoRVlcppT6ulPp3pdQUJs7kEZz7eVcEnquU+lul1PeUUkvADzFduNbz\nrLyvSV0eDusOpkveg3XiCq31NEborHVNSvXXEigATqsdtNb/gRGMz8B0OZxQSn1SKfXEc5+KIAjC\nzkYEjCAIwtbxWszIZB9j/V12Wxml5SZp65kDpZm3pNpFrRI53p8AT6v7PD38nDlHea3Kb4aDEQvn\nS6JJWs0zTCn1Voyx/nhM97J3YALgv7mO8m8BPgccxHQX+3WMqFjv6F7Nzslh9Xe7kGuynmvegNb6\ntYDCzK9zAjPQwL8ppd6wkfIEQRB2ChIDIwiCsEVorU8ppd4D/AEmaDsqOKrekvq5O857BvZzMKCU\nioVdx6pcGS4fxHTpAihqrWviPJRSj8XMl1I4z2OeBJ6ulOqIeg6UUh5m0srbzrO8h4ErmqRXvUco\npeKYYPVbtdbPjWYKZ7VvKfaUUj+N6aZWPyLcWl20WtYl3NfBeF9uCZNOAjeEXbmCSL5BTHzP6Hkc\n65wopfox8VG3Y+Kd3quUGgL+N6ar3Ac383iCIAgXE/HACIIgbC0fwIwa9Zy69DnM2/njdekvZAOz\ny69BHPPmHQClVBJ4FSZ+5j6t9TgmqP/lYexLNV8XZiSxj2qtK5wfX8E8X95Ul/4bmHiSrzTssTZf\nAm5SSj0+Ur/D1F7TFJDEXGsi+Z6O8UJEX9jVd7WrnndNNzCMBw3W97LvpeG1rfIyjDD5Urj+FYw4\n/bW6/d6G+b1v4cKoUPtM/2Xg69FR5LTWk5hhpZt58wRBEHYN4oERBEHYQrTWJaXUqzHDCEfTc+Eo\nVy9QSn0EIyKehzG2N5MM8EGl1DGM8foyTDxG1Ph/PSYg/vvhqF3LwCuBYeDnzveAWutblFJfAd6u\nlLoM04XrUeGxv03tsMHr4Y8wwyzfqpT6EGYY51djvEcd4THnlVLfBV6llMphYmaqx8xV84XMYLp0\nvTX8Db6FGV3tI+F1ygDPxlyjQt2+rRgEvq2U+hRwOWZUs9u11l8It/8PjHj5RDjJ6V2YUeR+Hvj8\nGiOQrZcZwFNKvR3T1j6LEZBfUUp9FBPT9J/Dz1sv8FiCIAjbinhgBEEQNo+mnhOt9W0Yb0Y9rwI+\nA/wSxkhfAJ6/3nLXSI8yCfwiZpSrDwAl4Fla63+J1O8bmAki78EYt38Q1uXZ4WAE0eOt1zv0Asyc\nKI8DPoQZivndwNPqRjE7Z5la6yVMXMstGK/ImzAG+l/WZX0hZu6TV2JGQHssRpy9BdOVrioOP4+Z\nHPRVwBvCQPrnYGJ93omZB6cdeCbGc7KewPf3Ad8Lz/GFmJHfVkRi2IXvycBHMb/xn2AGF3gD5vdf\ni/X8/n+OGXTgd4GXaq1ngKdi5uF5NWao7EcCv6G13qyR8QRBELYFKwg2s6eCIAiCIOwflFJHMTE6\nNfEzgiAIwtYhHhhBEARBEARBEHYNImAEQRAEQRAEQdg1iIARBEEQhAvjfGKDBEEQhAtEYmAEQRAE\nQRAEQdg1iAdGEARBEARBEIRdgwgYQRAEQRAEQRB2DSJgBEEQBEEQBEHYNYiAEQRBEARBEARh1yAC\nRhAEQRAEQRCEXYMIGEEQBEEQBEEQdg3/F2K+lJMn8V9wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11caf5190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='x', y='y', data=huge_k_means_data, order=2, \n",
    "            label='Sklearn K-Means', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=huge_dbscan_data, order=2, \n",
    "            label='Sklearn DBSCAN', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=huge_scipy_k_means_data, order=2, \n",
    "            label='Scipy K-Means', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=huge_hdbscan_data, order=2, \n",
    "            label='HDBSCAN', x_estimator=np.mean)\n",
    "sns.regplot(x='x', y='y', data=huge_fastcluster_data, order=2, \n",
    "            label='Fastcluster', x_estimator=np.mean)\n",
    "\n",
    "\n",
    "plt.gca().axis([0, 200000, 0, 240])\n",
    "plt.gca().set_xlabel('Number of data points')\n",
    "plt.gca().set_ylabel('Time taken to cluster (s)')\n",
    "plt.title('Performance Comparison of K-Means and DBSCAN')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the some differences become clear. The asymptotic complexity starts to kick in with fastcluster failing to keep up. In turn HDBSCAN and DBSCAN, while having sub-$O(n^2)$ complexity, can't achieve $O(n \\log(n))$ at this dataset dimension, and start to curve upward precipitously. Finally it demonstrates again how much of a difference implementation can make: the sklearn implementation of K-Means is far better than the scipy implementation. Since HDBSCAN clustering is a lot better than K-Means (unless you have good reasons to assume that the clusters partition your data and are all drawn from Gaussian distributions) and the scaling is still pretty good I would suggest that unless you have a truly stupendous amount of data you wish to cluster then the HDBSCAN implementation is a good choice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## But should I get a coffee?\n",
    "\n",
    "So we know which implementations scale and which don't; a more useful thing to know in practice is, given a dataset, what can I run interactively? What can I run while I go and grab some coffee? How about a run over lunch? What if I'm willing to wait until I get in tomorrow morning? Each of these represent significant breaks in productivity -- once you aren't working interactively anymore your productivity drops measurably, and so on.\n",
    "\n",
    "We can build a table for this. To start we'll need to be able to approximate how long a given clustering implementation will take to run. Fortunately we already gathered a lot of that data; if we load up the `statsmodels` package we can fit the data (with a quadratic or $n\\log n$ fit depending on the implementation; DBSCAN and HDBSCAN get caught here, since while they are under $O(n^2)$ scaling, they don't have an easily described model, so I'll model them as $n^2$ for now) and use the resulting model to make our predictions. Obviously this has some caveats: if you fill your RAM with a distance matrix your runtime isn't going to fit the curve.\n",
    "\n",
    "I've hand built a `time_samples` list to give a reasonable set of potential data sizes that are nice and human readable. After that we just need a function to fit and build the curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as sm\n",
    "\n",
    "time_samples = [1000, 2000, 5000, 10000, 25000, 50000, 75000, 100000, 250000, 500000, 750000,\n",
    "               1000000, 2500000, 5000000, 10000000, 50000000, 100000000, 500000000, 1000000000]\n",
    "\n",
    "def get_timing_series(data, quadratic=True):\n",
    "    if quadratic:\n",
    "        data['x_squared'] = data.x**2\n",
    "        model = sm.ols('y ~ x + x_squared', data=data).fit()\n",
    "        predictions = [model.params.dot([1.0, i, i**2]) for i in time_samples]\n",
    "        return pd.Series(predictions, index=pd.Index(time_samples))\n",
    "    else: # assume n log(n)\n",
    "        data['xlogx'] = data.x * np.log(data.x)\n",
    "        model = sm.ols('y ~ x + xlogx', data=data).fit()\n",
    "        predictions = [model.params.dot([1.0, i, i*np.log(i)]) for i in time_samples]\n",
    "        return pd.Series(predictions, index=pd.Index(time_samples))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we run that for each of our pre-existing datasets to extrapolate out predicted performance on the relevant dataset sizes. A little pandas wrangling later and we've produced a table of roughly how large a dataset you can tackle in each time frame with each implementation. I had to leave out the scipy KMeans timings because the noise in timing results caused the model to be unrealistic at larger data sizes. Note how the $O(n\\log n)$ algorithms utterly dominate here. In the meantime, for medium sizes data sets you can still get quite a lot done with HDBSCAN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Interactive</th>\n",
       "      <th>Get Coffee</th>\n",
       "      <th>Over Lunch</th>\n",
       "      <th>Overnight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AffinityPropagation</th>\n",
       "      <td>2000</td>\n",
       "      <td>10000</td>\n",
       "      <td>25000</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spectral</th>\n",
       "      <td>2000</td>\n",
       "      <td>5000</td>\n",
       "      <td>25000</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Agglomerative</th>\n",
       "      <td>2000</td>\n",
       "      <td>10000</td>\n",
       "      <td>25000</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeBaCl</th>\n",
       "      <td>5000</td>\n",
       "      <td>25000</td>\n",
       "      <td>75000</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScipySingleLinkage</th>\n",
       "      <td>25000</td>\n",
       "      <td>50000</td>\n",
       "      <td>100000</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fastcluster</th>\n",
       "      <td>50000</td>\n",
       "      <td>100000</td>\n",
       "      <td>500000</td>\n",
       "      <td>1000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDBSCAN</th>\n",
       "      <td>100000</td>\n",
       "      <td>500000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>5000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DBSCAN</th>\n",
       "      <td>75000</td>\n",
       "      <td>250000</td>\n",
       "      <td>1000000</td>\n",
       "      <td>2500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKLearn KMeans</th>\n",
       "      <td>1000000000</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>1000000000</td>\n",
       "      <td>1000000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Interactive  Get Coffee  Over Lunch   Overnight\n",
       "AffinityPropagation         2000       10000       25000      100000\n",
       "Spectral                    2000        5000       25000       75000\n",
       "Agglomerative               2000       10000       25000      100000\n",
       "DeBaCl                      5000       25000       75000      250000\n",
       "ScipySingleLinkage         25000       50000      100000      250000\n",
       "Fastcluster                50000      100000      500000     1000000\n",
       "HDBSCAN                   100000      500000     1000000     5000000\n",
       "DBSCAN                     75000      250000     1000000     2500000\n",
       "SKLearn KMeans        1000000000  1000000000  1000000000  1000000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ap_timings = get_timing_series(ap_data)\n",
    "spectral_timings = get_timing_series(spectral_data)\n",
    "agg_timings = get_timing_series(agg_data)\n",
    "debacl_timings = get_timing_series(debacl_data)\n",
    "fastclust_timings = get_timing_series(large_fastclust_data.ix[:10,:].copy())\n",
    "scipy_single_timings = get_timing_series(large_scipy_single_data.ix[:10,:].copy())\n",
    "hdbscan_boruvka = get_timing_series(huge_hdbscan_data, quadratic=True)\n",
    "#scipy_k_means_timings = get_timing_series(huge_scipy_k_means_data, quadratic=False)\n",
    "dbscan_timings = get_timing_series(huge_dbscan_data, quadratic=True)\n",
    "k_means_timings = get_timing_series(huge_k_means_data, quadratic=False)\n",
    "\n",
    "timing_data = pd.concat([ap_timings, spectral_timings, agg_timings, debacl_timings, \n",
    "                            scipy_single_timings, fastclust_timings, hdbscan_boruvka, \n",
    "                            dbscan_timings, k_means_timings\n",
    "                           ], axis=1)\n",
    "timing_data.columns=['AffinityPropagation', 'Spectral', 'Agglomerative',\n",
    "                                       'DeBaCl', 'ScipySingleLinkage', 'Fastcluster',\n",
    "                                       'HDBSCAN', 'DBSCAN', 'SKLearn KMeans'\n",
    "                                      ]\n",
    "def get_size(series, max_time):\n",
    "    return series.index[series < max_time].max()\n",
    "\n",
    "datasize_table = pd.concat([\n",
    "                            timing_data.apply(get_size, max_time=30),\n",
    "                            timing_data.apply(get_size, max_time=300),\n",
    "                            timing_data.apply(get_size, max_time=3600),\n",
    "                            timing_data.apply(get_size, max_time=8*3600)\n",
    "                            ], axis=1)\n",
    "datasize_table.columns=('Interactive', 'Get Coffee', 'Over Lunch', 'Overnight')\n",
    "datasize_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions\n",
    "\n",
    "Performance obviously depends on the algorithm chosen, but can also vary significantly upon the specific implementation (HDBSCAN is far better hierarchical density based clustering than DeBaCl, and sklearn has by far the best K-Means implementation). For anything beyond toy datasets, however, your algorithm options are greatly constrained. In my (obviously biased) opinion [HDBSCAN is the best algorithm for clustering](http://nbviewer.jupyter.org/github/scikit-learn-contrib/hdbscan/blob/master/notebooks/Comparing%20Clustering%20Algorithms.ipynb). If you need to cluster data beyond the scope that HDBSCAN can reasonably handle then the only algorithm options on the table are DBSCAN and K-Means; DBSCAN is the slower of the two, especially for very large data, but K-Means clustering can be remarkably poor -- it's a tough choice."
   ]
  }
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